The effect of trust on travel agent online use: Application of the technology acceptance model
Bibliographic record
Abstract
Nowadays, shopping for travel products through online travel agent has become very popular. This study aims to explain the effects of perceived ease of use, perceived usefulness, and trust on attitudes and intentions to reuse online travel agents. The population of this research is the users of the online travel agent Traveloka application in the city of Denpasar. The sample in this study was taken using a non-probability sampling method with a total of 200 respondents. Data collection was carried out using survey methods. The data obtained were then processed using SEM-PLS analysis tools. This study found that perceived ease of use had a positive and significant effect on perceived usefulness and attitude toward using from the Traveloka website. Perceived usefulness had a positive and significant effect on attitude toward using the Traveloka.com website. Trust had a positive and significant effect on perceived usefulness and attitude toward using from the Traveloka website. Attitude toward using had a positive and significant effect on the intention to reuse the Traveloka.com website. This research also proves that attitude toward use of online facilities mediates the influence of perceived ease of use, perceived usefulness and trust on the intention to reuse the Traveloka.com website.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".